@inproceedings{wang-etal-2026-entropy-guided,
title = "Entropy-Guided Token Dropout: Training Autoregressive Language Models with Limited Domain Data",
author = "Wang, Jiapeng and
Hu, Yiwen and
Gao, Yanzipeng and
Wang, Haoyu and
Wang, Shuo and
Lu, Hongyu and
Mao, Jiaxin and
Zhao, Xin and
Li, Junyi and
Zhang, Xiao",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.902/",
pages = "20783--20799",
ISBN = "979-8-89176-459-0",
abstract = "As access to high-quality, domain-specific data grows increasingly scarce, multi-epoch training has become a practical strategy for adapting large language models (LLMs). However, autoregressive models often suffer from performance degradation under repeated data exposure, where overfitting leads to a marked decline in model capability. Through empirical analysis, we trace this degradation to an imbalance in learning dynamics: predictable, low-entropy tokens are learned quickly and come to dominate optimization, while the model{'}s ability to generalize on high-entropy tokens deteriorates with continued training. To address this, we introduce \textbf{EntroDrop}, an entropy-guided token dropout method that functions as structured data regularization. EntroDrop selectively masks low-entropy tokens during training and employs a curriculum schedule to adjust regularization strength in alignment with training progress. Experiments across model scales from 0.6B to 8B parameters show that EntroDrop consistently outperforms standard regularization baselines and maintains robust performance throughout extended multi-epoch training. These findings underscore the importance of aligning regularization with token-level learning dynamics when training on limited data. Our approach offers a promising pathway toward more effective adaptation of LLMs in data-constrained domains."
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<abstract>As access to high-quality, domain-specific data grows increasingly scarce, multi-epoch training has become a practical strategy for adapting large language models (LLMs). However, autoregressive models often suffer from performance degradation under repeated data exposure, where overfitting leads to a marked decline in model capability. Through empirical analysis, we trace this degradation to an imbalance in learning dynamics: predictable, low-entropy tokens are learned quickly and come to dominate optimization, while the model’s ability to generalize on high-entropy tokens deteriorates with continued training. To address this, we introduce EntroDrop, an entropy-guided token dropout method that functions as structured data regularization. EntroDrop selectively masks low-entropy tokens during training and employs a curriculum schedule to adjust regularization strength in alignment with training progress. Experiments across model scales from 0.6B to 8B parameters show that EntroDrop consistently outperforms standard regularization baselines and maintains robust performance throughout extended multi-epoch training. These findings underscore the importance of aligning regularization with token-level learning dynamics when training on limited data. Our approach offers a promising pathway toward more effective adaptation of LLMs in data-constrained domains.</abstract>
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%0 Conference Proceedings
%T Entropy-Guided Token Dropout: Training Autoregressive Language Models with Limited Domain Data
%A Wang, Jiapeng
%A Hu, Yiwen
%A Gao, Yanzipeng
%A Wang, Haoyu
%A Wang, Shuo
%A Lu, Hongyu
%A Mao, Jiaxin
%A Zhao, Xin
%A Li, Junyi
%A Zhang, Xiao
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F wang-etal-2026-entropy-guided
%X As access to high-quality, domain-specific data grows increasingly scarce, multi-epoch training has become a practical strategy for adapting large language models (LLMs). However, autoregressive models often suffer from performance degradation under repeated data exposure, where overfitting leads to a marked decline in model capability. Through empirical analysis, we trace this degradation to an imbalance in learning dynamics: predictable, low-entropy tokens are learned quickly and come to dominate optimization, while the model’s ability to generalize on high-entropy tokens deteriorates with continued training. To address this, we introduce EntroDrop, an entropy-guided token dropout method that functions as structured data regularization. EntroDrop selectively masks low-entropy tokens during training and employs a curriculum schedule to adjust regularization strength in alignment with training progress. Experiments across model scales from 0.6B to 8B parameters show that EntroDrop consistently outperforms standard regularization baselines and maintains robust performance throughout extended multi-epoch training. These findings underscore the importance of aligning regularization with token-level learning dynamics when training on limited data. Our approach offers a promising pathway toward more effective adaptation of LLMs in data-constrained domains.
%U https://aclanthology.org/2026.emnlp-main.902/
%P 20783-20799
Markdown (Informal)
[Entropy-Guided Token Dropout: Training Autoregressive Language Models with Limited Domain Data](https://aclanthology.org/2026.emnlp-main.902/) (Wang et al., EMNLP 2026)
ACL
- Jiapeng Wang, Yiwen Hu, Yanzipeng Gao, Haoyu Wang, Shuo Wang, Hongyu Lu, Jiaxin Mao, Xin Zhao, Junyi Li, and Xiao Zhang. 2026. Entropy-Guided Token Dropout: Training Autoregressive Language Models with Limited Domain Data. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 20783–20799, Budapest, Hungary. Association for Computational Linguistics.